ENGR.ECONOMIC ANALYSIS
14th Edition
ISBN: 9780190931919
Author: NEWNAN
Publisher: Oxford University Press
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What does the value for the coefficient mean in the regression analysis of both the company ?
P-Values - how its related to customer satisfaction in the regression analysis of both the company ?
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- 4arrow_forwardAn analyst working for your firm provided an estimated log-linear demand function based on the natural logarithm of the quantity sold, price, and the average income of consumers. Results are summarized in the following table: SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression Residual Total Intercept LN Price LN Income df 0.968 0.937 0.933 0.003 30 SS MS F 2 0.003637484 0.001818742 202.48598 0.000242516 8.98206E-06 27 29 0.00388 Coefficients Standard Error 0.57 0.00 0.13 0.51 -0.08 0.15 t Stat 0.90 -19.50 1.13 P-value 0.37 0.00 0.27 Significance F 5.55598E-17 Lower 95% -0.65 -0.09 -0.12 How would a 4 percent increase in income impact the demand for your product? Demand would increase by 60 percent. Demand would increase by 0.6 percent. Demand would decrease by 60 percent. Demand would decrease by 0.6 percent. Upper 95% 1.68 -0.07 0.41arrow_forwardThe dependent variable in the regression in our cost driver analysis is which of the following? Company sales Total overhead cost for the entire period of time Total overhead cost per montharrow_forward
- Consider the following regression estimates (FN2) Linear regression belavg abvavg female married _cons b. 1 wage O C. 5 O d. 4 Robust Coef. Std. Err. 3047845 3150202 2820787 -1.063254 .0693348 -2.751963 .9686236 .2612646 6.699098 .2889831 Number of obs F(4, 1255) Prob > F R-squared Root MSE t P>|t| -3.49 0.001 0.22 0.826 -9.76 0.000 3.71 0.000 23.18 0.000 |||||||||| = = .45606 6.132155 = -1.661197 -.5486894 -3.305361 = = [95% Conf. Interval] 1,260 56.35 0.0000 0.1121 4.3987 assume that MLR 1-6 hold. In the regression above, how many coefficients (including the constant) are statistically significant at the 1% level? a. 3 -.4653108 .687359 -2.198565 1.481187 7.266042arrow_forwardEach term (3 months) the current group of economics students completed a questionnaire as to how much they would spend on new purchases compared to how much they would save/pay off bills, if they suddenly and unexpectedly received a check for $1,000. The average MPC is shown in the table below. Month in which student poll was taken Average of students’ responses as MPC March 0.41 June 0.30 September 0.22 December 0.56 What do these MPC’s imply about the students’ thinking over the course of the year? (Enter response here.) What is likely happening in the economy during the same period of time?arrow_forwardChegg Home Expert Q&A My solutions Student question Time Left: 00:09:16 Use the data set Home Morloage Disdosure Act Data (HMDA) from the AER package. Subtract 1 from the factor "Was the mortage denied?". Then, eatimate the Inear regression model of paymentsto - income rato on the factor "Was the mortage denied?" ising Ols. Based on these results, the prebabity a mertougo will be denled if the payment - to - income ratio is equal to 2 is a. 1.13 b. 0.107 c. None of these d. 0.52 e. 1.00 Use the data set Home Mortgage Disclosure Act Data (HMDA) from the AER package. Subtract 1 from the factor "Was the mortage denied?". Then, estimate the linear regression model of payments-to-income ratio on the factor "Was the mortage denied?" using OLS. Based on these results, the probability a mortgage will be denied if the payment-to-income ratio is equal to 2 is a. 1.13 Ob.0.87 Oc None of these d.0.52 1.00arrow_forward
- 3) identification and analysis of coefficient of correlation identification and analysis of coefficient of determinationarrow_forwardConsider the following computer output of a multiple regression analysis relating annual salary to years of education and years of work experience. Regression Statistics Multiple R 0.7339 R Square 0.5386 Adjusted R Square 0.5185 Standard Error 2137.5200 Observations 49 ANOVA SS df Regression 2 245,370,679.3850 122,685,339.6925 26.8517 MS F Significance F 1.9E-08 Total Residual 46 210,173,612.6150 48 455,544,292.0000 4,568,991.5786 Coefficients Standard Error Intercept Education (Years) 14290.37278 2350.8671 2,528.5819 338.1140 Experience (Years) 829.3167 392.5627 t Stat P-value 5.6515 0.000000961 9200.6014 6.9529 0.000000011 2.1126 0.040093183 Lower 95 % Upper 95% 19,380.1442 1670.2789 3031.4553 39.129 1619.5044 Step 2 of 2: How much would you expect your salary to increase if you had one more year of education?arrow_forwardExplain the OLS Estimator in Multiple Regression in detail?arrow_forward
- Explain with clear brief points with correct answer - please do not attempt to answer if you are not confident please do not waste my chance to ask questions as they are not for free and please do not сору. Question - Are there any implications of the finding for policy makers or business leaders?arrow_forwardcalculate slope coefficient for a regression of Y on X calculate the constant of a regression of Y on X calculate the residual for the first observation in the tablearrow_forwardQUESTION 7 Which is NOT true about the coefficient of determination? As you add more variables, the R-square generally rises. As you add more variables, the adjusted R-square can fall. If the R-square is above 50%, the regression is considered significant. The R-square gives the percent of the variation in the dependent variable that is explained by the independent variables. The higher is the R-square, the better is the fit.arrow_forward
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